Over 50 million k-12 students will go back to school in the US this month. For many of them using a school computer, every word they type will be tracked. Under the Children's Internet Protection Act (CIPA), any US school that receives federal funding is required to have an internet-safety policy. As school-issued tablets and Chromebook laptops become more commonplace, schools must install technological guardrails to keep their students safe. For some, this simply means blocking inappropriate websites.

New teachers, new backpacks, new crushes--and algorithms trawling students' social media posts. Blake Prewitt, superintendent of Lakeview school district in Battle Creek, Michigan, says he typically wakes up each morning to twenty new emails from a social media monitoring system the district activated earlier this year. It uses keywords and machine learning algorithms to flag public posts on Twitter and other networks that contain language or images that may suggest conflict or violence, and tag or mention district schools or communities. In recent months the alert emails have included an attempted abduction outside one school--Prewitt checked if the school's security cameras could aid police--and a comment about dress code from a student's relative--district staff contacted the family. Prewitt says the alerts help him keep his 4,000 students and 500 staff safe.

Quantitative asset management company WorldQuant, in partnership with global online learning company Udacity, has launched a new Artificial Intelligence for Trading Nanodegree program. Students enrolled in the programme will analyse real data and build financial models by learning the basics of quantitative trading, as well as how to analyse alternative data and use machine learning to generate trading signals. Udacity and WorldQuant have collaborated with top industry professionals with prior experience at leading financial institutions to ensure students are exposed to the latest AI applications in trading and quantitative finance. By learning from industry experts, students will advance their finance knowledge, build a strong portfolio of real-world projects and learn to generate trading signals using natural language processing, recurrent neural networks and random forests. Graduates will gain the quantitative skills currently in demand across multiple functions and roles at hedge funds, investment banks and fintech startups.

Artificial intelligence startup founder Joel Hellermark is pioneering the development of algorithms that will radically transform education. Using deep neural networks, algorithms personalize educational content in real time based on how the student learns, what they've already mastered, and what works best for similar students. Joel Hellermark is the founder and CEO of Sana which is an artificial intelligence company that applies recent breakthroughs in deep learning to personalise education. The Sana team is made up of engineers and scientists with backgrounds ranging from Imperial College and CERN to Google and Spotify.

An attendee looks at a Tifana.com Co. AI service character displayed on a screen at the Artificial Intelligence Exhibition & Conference in Tokyo, Japan, on Wednesday, April 4, 2018. The AI Expo will run through April 6. It's generally accepted that as technology moves into classrooms, teachers will move, as the saying goes, "from a sage on the stage to a guide on side." That shift has rightly troubled teachers and teaching advocates who fear that educators who instruct, analyze and provide vital context will be diminished or co-opted outright by soulless, algorithm-driven tech.

Reinforcement learning methods have been used for learning dialogue policies from the experience of conversations. However, learning an effective dialogue policy frequently requires prohibitively many conversations. This is partly because of the sparse rewards in dialogues, and the relatively small number of successful dialogues in early learning phase. Hindsight experience replay (HER) enables an agent to learn from failure, but the vanilla HER is inapplicable to dialogue domains due to dialogue goals being implicit (c.f., explicit goals in manipulation tasks). In this work, we develop two complex HER methods providing different trade-offs between complexity and performance. Experiments were conducted using a realistic user simulator. Results suggest that our HER methods perform better than standard and prioritized experience replay methods (as applied to deep Q-networks) in learning rate, and that our two complex HER methods can be combined to produce the best performance.

Recently, network lasso has drawn many attentions due to its remarkable performance on simultaneous clustering and optimization. However, it usually suffers from the imperfect data (noise, missing values etc), and yields sub-optimal solutions. The reason is that it finds the similar instances according to their features directly, which is usually impacted by the imperfect data, and thus returns sub-optimal results. In this paper, we propose triangle lasso to avoid its disadvantage. Triangle lasso finds the similar instances according to their neighbours. If two instances have many common neighbours, they tend to become similar. Although some instances are profiled by the imperfect data, it is still able to find the similar counterparts. Furthermore, we develop an efficient algorithm based on Alternating Direction Method of Multipliers (ADMM) to obtain a moderately accurate solution. In addition, we present a dual method to obtain the accurate solution with the low additional time consumption. We demonstrate through extensive numerical experiments that triangle lasso is robust to the imperfect data. It usually yields a better performance than the state-of-the-art method when performing data analysis tasks in practical scenarios.

Over 50 million k-12 students will go back to school in the US this month. For many of them using a school computer, every word they type will be tracked. Under the Children's Internet Protection Act (CIPA), any US school that receives federal funding is required to have an internet-safety policy. As school-issued tablets and Chromebook laptops become more commonplace, schools must install technological guardrails to keep their students safe. For some, this simply means blocking inappropriate websites.

Citrine is a company that builds data infrastructure and predictive data analysis software for the materials industry. Machine learning is a key tool in our toolbox. I have had a few professors and students in materials departments ask me (1) how machine learning could help in their research; and (2) how to quickly come up to speed in machine learning without going back to school for a degree in computer science. While a variety of machine learning courses and how-tos exist on the web already (see here, here, or here), none are specific to the field of materials science. I think the best way to master a new concept is by directly applying it, so this tutorial will show you how to build a machine learning-based model of a canonical solid-state materials property: band gap.

You've got a classroom filled with middle school students working out math problems on computers. Which students are knocking them out with ease? In a pilot test of math software at David E. Williams (DEW) Middle School in Coraopolis, Pa., emojis tell the smart-glasses-wearing teacher what she needs to know. A smiling emoji hovers over a student's head: That means the student is progressing nicely. A frown emoji indicates struggle.